An Implementation of Sensitivity Analysis in Bayesian Networks

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. References: H. Chan, A. Darwiche (2002) ; R.G. Cowell, R.J. Verrall, Y.K. Yoon (2007) ; C. Goergen, M. Leonelli (2020) .


Reference manual

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0.1.1 by Manuele Leonelli, 7 months ago

Browse source code at

Authors: Manuele Leonelli [aut, cre] , Ramsiya Ramanathan [aut] , Rachel Wilkerson [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports bnlearn, dplyr, ggplot2, gRain, gRbase, graphics, matrixcalc, purrr, qgraph, RColorBrewer, reshape2, rlang, tidyr

Suggests testthat, knitr, rmarkdown

See at CRAN